Loading report..

Highlight Samples

This report has flat image plots that won't be highlighted.
See the documentation for help.

Regex mode off

    Rename Samples

    This report has flat image plots that won't be renamed.
    See the documentation for help.

    Click here for bulk input.

    Paste two columns of a tab-delimited table here (eg. from Excel).

    First column should be the old name, second column the new name.

    Regex mode off

      Show / Hide Samples

      This report has flat image plots that won't be hidden.
      See the documentation for help.

      Regex mode off

        Export Plots

        px
        px
        X

        Download the raw data used to create the plots in this report below:

        Note that additional data was saved in multiqc_data when this report was generated.


        Choose Plots

        If you use plots from MultiQC in a publication or presentation, please cite:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        Save Settings

        You can save the toolbox settings for this report to the browser.


        Load Settings

        Choose a saved report profile from the dropdown box below:

        Tool Citations

        Please remember to cite the tools that you use in your analysis.

        To help with this, you can download publication details of the tools mentioned in this report:

        About MultiQC

        This report was generated using MultiQC, version 1.15

        You can see a YouTube video describing how to use MultiQC reports here: https://youtu.be/qPbIlO_KWN0

        For more information about MultiQC, including other videos and extensive documentation, please visit http://multiqc.info

        You can report bugs, suggest improvements and find the source code for MultiQC on GitHub: https://github.com/ewels/MultiQC

        MultiQC is published in Bioinformatics:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.

        This report has been generated by the nf-core/mag analysis pipeline. For information about how to interpret these results, please see the documentation.

        Report generated on 2024-02-26, 12:29 CET based on data in: /cephyr/NOBACKUP/groups/jbp/matev/OceanARM/BlendARGs/work/0b/5dda873df603f31e872a2f47ca9ecb


        General Statistics

        Showing 6944 samples.

        loading..

        FastQC: raw reads

        FastQC: raw reads is a quality control tool for high throughput sequence data, written by Simon Andrews at the Babraham Institute in Cambridge.

        Sequence Counts

        Sequence counts for each sample. Duplicate read counts are an estimate only.

        This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

        You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Sequence Quality Histograms

        The mean quality value across each base position in the read.

        To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

        Taken from the FastQC help:

        The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Per Sequence Quality Scores

        The number of reads with average quality scores. Shows if a subset of reads has poor quality.

        From the FastQC help:

        The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Per Base Sequence Content

        The proportion of each base position for which each of the four normal DNA bases has been called.

        To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

        To see the data as a line plot, as in the original FastQC graph, click on a sample track.

        From the FastQC help:

        Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

        In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

        It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

        Click a sample row to see a line plot for that dataset.
        Rollover for sample name
        Position: -
        %T: -
        %C: -
        %A: -
        %G: -

        Per Sequence GC Content

        The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

        From the FastQC help:

        This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

        In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

        An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Per Base N Content

        The percentage of base calls at each position for which an N was called.

        From the FastQC help:

        If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

        It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Sequence Length Distribution

        All samples have sequences of a single length (150bp).

        Sequence Duplication Levels

        The relative level of duplication found for every sequence.

        From the FastQC Help:

        In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (eg PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Overrepresented sequences

        The total amount of overrepresented sequences found in each library.

        FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as over represented.

        Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

        From the FastQC Help:

        A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

        FastQC lists all of the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

        392 samples had less than 1% of reads made up of overrepresented sequences

        Adapter Content

        The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

        Note that only samples with ≥ 0.1% adapter contamination are shown.

        There may be several lines per sample, as one is shown for each adapter detected in the file.

        From the FastQC Help:

        The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Status Checks

        Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

        Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

        In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

        loading..

        fastp

        fastp An ultra-fast all-in-one FASTQ preprocessor (QC, adapters, trimming, filtering, splitting...).DOI: 10.1093/bioinformatics/bty560.

        Filtered Reads

        Filtering statistics of sampled reads.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Insert Sizes

        Insert size estimation of sampled reads.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Sequence Quality

        Average sequencing quality over each base of all reads.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        GC Content

        Average GC content over each base of all reads.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        N content

        Average N content over each base of all reads.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        FastQC: after preprocessing

        FastQC: after preprocessing After trimming and, if requested, contamination removal.

        Sequence Counts

        Sequence counts for each sample. Duplicate read counts are an estimate only.

        This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

        You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Sequence Quality Histograms

        The mean quality value across each base position in the read.

        To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

        Taken from the FastQC help:

        The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Per Sequence Quality Scores

        The number of reads with average quality scores. Shows if a subset of reads has poor quality.

        From the FastQC help:

        The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Per Base Sequence Content

        The proportion of each base position for which each of the four normal DNA bases has been called.

        To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

        To see the data as a line plot, as in the original FastQC graph, click on a sample track.

        From the FastQC help:

        Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

        In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

        It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

        Click a sample row to see a line plot for that dataset.
        Rollover for sample name
        Position: -
        %T: -
        %C: -
        %A: -
        %G: -

        Per Sequence GC Content

        The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

        From the FastQC help:

        This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

        In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

        An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Per Base N Content

        The percentage of base calls at each position for which an N was called.

        From the FastQC help:

        If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

        It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Sequence Length Distribution

        The distribution of fragment sizes (read lengths) found. See the FastQC help

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Sequence Duplication Levels

        The relative level of duplication found for every sequence.

        From the FastQC Help:

        In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (eg PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Overrepresented sequences

        The total amount of overrepresented sequences found in each library.

        FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as over represented.

        Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

        From the FastQC Help:

        A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

        FastQC lists all of the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

        392 samples had less than 1% of reads made up of overrepresented sequences

        Adapter Content

        The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

        Note that only samples with ≥ 0.1% adapter contamination are shown.

        There may be several lines per sample, as one is shown for each adapter detected in the file.

        From the FastQC Help:

        The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

        loading..

        Status Checks

        Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

        Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

        In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

        loading..

        Bowtie2: PhiX removal

        Mapping statistics of reads mapped against PhiX and subsequently removed.DOI: 10.1038/nmeth.1923; 10.1038/nmeth.3317; 10.1038/s41587-019-0201-4.

        Paired-end alignments

        This plot shows the number of reads aligning to the reference in different ways.

        Please note that single mate alignment counts are halved to tally with pair counts properly.

        There are 6 possible types of alignment:

        • PE mapped uniquely: Pair has only one occurence in the reference genome.
        • PE mapped discordantly uniquely: Pair has only one occurence but not in proper pair.
        • PE one mate mapped uniquely: One read of a pair has one occurence.
        • PE multimapped: Pair has multiple occurence.
        • PE one mate multimapped: One read of a pair has multiple occurence.
        • PE neither mate aligned: Pair has no occurence.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Bowtie2: assembly

        Mapping statistics of reads mapped against assemblies.DOI: 10.1038/nmeth.1923; 10.1038/nmeth.3317; 10.1038/s41587-019-0201-4.

        Paired-end alignments

        This plot shows the number of reads aligning to the reference in different ways.

        Please note that single mate alignment counts are halved to tally with pair counts properly.

        There are 6 possible types of alignment:

        • PE mapped uniquely: Pair has only one occurence in the reference genome.
        • PE mapped discordantly uniquely: Pair has only one occurence but not in proper pair.
        • PE one mate mapped uniquely: One read of a pair has one occurence.
        • PE multimapped: Pair has multiple occurence.
        • PE one mate multimapped: One read of a pair has multiple occurence.
        • PE neither mate aligned: Pair has no occurence.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        BUSCO

        BUSCO assesses genome assembly and annotation completeness with Benchmarking Universal Single-Copy Orthologs. In case BUSCO's automated lineage selection was used, only generic results for the selected domain are shown and only for genome bins and kept, unbinned contigs for which the BUSCO analysis was successfull, i.e. not for contigs for which no BUSCO genes could be found. Bins for which a specific virus lineage was selected are also not shown.DOI: 10.1093/bioinformatics/btv351.

        Lineage: bacteria_odb10

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        QUAST: assembly

        QUAST: assembly Assembly statistics of raw assemblies.DOI: 10.1093/bioinformatics/btt086.

        Assembly Statistics

        Showing 196/196 rows and 6/6 columns.
        Sample NameN50 (Kbp)N75 (Kbp)L50 (K)L75 (K)Largest contig (Kbp)Length (Mbp)
        MEGAHIT-S0001
        0.8Kbp
        0.6Kbp
        37.9K
        76.3K
        98.3Kbp
        107.4Mbp
        MEGAHIT-S0002
        1.1Kbp
        0.7Kbp
        68.5K
        163.3K
        153.5Kbp
        319.1Mbp
        MEGAHIT-S0003
        1.1Kbp
        0.7Kbp
        63.7K
        156.7K
        155.0Kbp
        309.6Mbp
        MEGAHIT-S0004
        0.9Kbp
        0.6Kbp
        81.5K
        180.6K
        101.3Kbp
        299.9Mbp
        MEGAHIT-S0005
        0.9Kbp
        0.6Kbp
        88.5K
        192.7K
        127.2Kbp
        315.2Mbp
        MEGAHIT-S0006
        0.8Kbp
        0.6Kbp
        99.0K
        189.1K
        23.0Kbp
        250.8Mbp
        MEGAHIT-S0008
        1.0Kbp
        0.7Kbp
        57.7K
        133.2K
        148.6Kbp
        236.2Mbp
        MEGAHIT-S0009
        0.9Kbp
        0.6Kbp
        61.8K
        139.1K
        176.9Kbp
        230.8Mbp
        MEGAHIT-S0010
        1.0Kbp
        0.7Kbp
        65.7K
        153.4K
        127.4Kbp
        275.5Mbp
        MEGAHIT-S0011
        1.0Kbp
        0.7Kbp
        97.2K
        224.6K
        156.3Kbp
        403.6Mbp
        MEGAHIT-S0012
        0.9Kbp
        0.7Kbp
        93.6K
        202.2K
        148.0Kbp
        335.0Mbp
        MEGAHIT-S0013
        0.9Kbp
        0.7Kbp
        107.0K
        226.5K
        95.6Kbp
        363.8Mbp
        MEGAHIT-S0015
        1.0Kbp
        0.7Kbp
        88.6K
        193.3K
        244.5Kbp
        327.6Mbp
        MEGAHIT-S0016
        1.0Kbp
        0.7Kbp
        85.4K
        189.0K
        325.5Kbp
        331.4Mbp
        MEGAHIT-S0017
        0.8Kbp
        0.6Kbp
        51.7K
        102.8K
        33.0Kbp
        146.7Mbp
        MEGAHIT-S0018
        0.8Kbp
        0.6Kbp
        57.2K
        113.7K
        36.3Kbp
        161.1Mbp
        MEGAHIT-S0019
        0.9Kbp
        0.6Kbp
        70.1K
        141.6K
        34.7Kbp
        206.5Mbp
        MEGAHIT-S0020
        0.8Kbp
        0.6Kbp
        48.8K
        94.0K
        20.7Kbp
        124.0Mbp
        MEGAHIT-S0021
        0.9Kbp
        0.7Kbp
        47.2K
        103.4K
        65.4Kbp
        172.8Mbp
        MEGAHIT-S0022
        1.0Kbp
        0.7Kbp
        45.5K
        100.0K
        106.7Kbp
        168.7Mbp
        MEGAHIT-S0023
        1.0Kbp
        0.7Kbp
        50.5K
        112.1K
        146.7Kbp
        192.8Mbp
        MEGAHIT-S0024
        0.9Kbp
        0.7Kbp
        58.2K
        123.1K
        76.7Kbp
        198.7Mbp
        MEGAHIT-S0025
        0.8Kbp
        0.6Kbp
        63.3K
        129.6K
        186.3Kbp
        189.3Mbp
        MEGAHIT-S0026
        0.9Kbp
        0.6Kbp
        50.1K
        108.3K
        78.3Kbp
        172.3Mbp
        MEGAHIT-S0027
        0.9Kbp
        0.6Kbp
        67.2K
        145.6K
        166.0Kbp
        235.0Mbp
        MEGAHIT-S0028
        0.9Kbp
        0.6Kbp
        72.0K
        149.2K
        81.9Kbp
        226.6Mbp
        MEGAHIT-S0029
        0.9Kbp
        0.6Kbp
        108.1K
        229.0K
        125.4Kbp
        364.7Mbp
        MEGAHIT-S0030
        0.8Kbp
        0.6Kbp
        84.5K
        171.0K
        35.9Kbp
        248.1Mbp
        MEGAHIT-S0031
        0.9Kbp
        0.6Kbp
        91.9K
        192.1K
        227.6Kbp
        296.3Mbp
        MEGAHIT-S0032
        0.9Kbp
        0.6Kbp
        87.3K
        180.1K
        381.9Kbp
        272.8Mbp
        MEGAHIT-S0033
        0.9Kbp
        0.6Kbp
        51.7K
        105.3K
        63.7Kbp
        155.8Mbp
        MEGAHIT-S0034
        0.8Kbp
        0.6Kbp
        64.5K
        123.0K
        78.1Kbp
        160.9Mbp
        MEGAHIT-S0035
        0.8Kbp
        0.6Kbp
        79.4K
        148.3K
        36.2Kbp
        184.8Mbp
        MEGAHIT-S0036
        1.0Kbp
        0.7Kbp
        50.2K
        115.9K
        277.5Kbp
        203.7Mbp
        MEGAHIT-S0037
        1.0Kbp
        0.7Kbp
        80.5K
        180.4K
        123.5Kbp
        314.7Mbp
        MEGAHIT-S0039
        0.9Kbp
        0.6Kbp
        67.7K
        139.2K
        91.6Kbp
        210.6Mbp
        MEGAHIT-S0040
        0.8Kbp
        0.6Kbp
        58.7K
        116.6K
        58.2Kbp
        162.3Mbp
        MEGAHIT-S0041
        0.9Kbp
        0.7Kbp
        49.1K
        106.4K
        97.9Kbp
        176.1Mbp
        MEGAHIT-S0042
        1.0Kbp
        0.7Kbp
        43.9K
        103.0K
        104.2Kbp
        194.4Mbp
        MEGAHIT-S0043
        1.0Kbp
        0.7Kbp
        88.6K
        196.7K
        512.5Kbp
        345.3Mbp
        MEGAHIT-S0044
        1.1Kbp
        0.7Kbp
        43.5K
        105.9K
        189.7Kbp
        211.3Mbp
        MEGAHIT-S0045
        0.8Kbp
        0.6Kbp
        87.8K
        166.9K
        49.2Kbp
        217.1Mbp
        MEGAHIT-S0046
        0.9Kbp
        0.7Kbp
        90.7K
        192.5K
        160.1Kbp
        312.5Mbp
        MEGAHIT-S0047
        0.8Kbp
        0.6Kbp
        82.9K
        165.8K
        65.1Kbp
        237.9Mbp
        MEGAHIT-S0048
        0.8Kbp
        0.6Kbp
        61.4K
        119.4K
        63.2Kbp
        162.3Mbp
        MEGAHIT-S0147
        1.0Kbp
        0.7Kbp
        48.2K
        110.8K
        165.8Kbp
        205.9Mbp
        MEGAHIT-S0148
        0.9Kbp
        0.6Kbp
        93.3K
        192.9K
        135.5Kbp
        291.6Mbp
        MEGAHIT-S0149
        0.9Kbp
        0.6Kbp
        50.6K
        105.7K
        83.6Kbp
        160.3Mbp
        MEGAHIT-S0150
        0.8Kbp
        0.6Kbp
        52.6K
        105.9K
        144.9Kbp
        150.1Mbp
        MEGAHIT-S0151
        0.8Kbp
        0.6Kbp
        54.3K
        105.6K
        72.1Kbp
        139.1Mbp
        MEGAHIT-S0152
        0.9Kbp
        0.7Kbp
        80.7K
        176.8K
        108.2Kbp
        296.4Mbp
        MEGAHIT-S0153
        0.8Kbp
        0.6Kbp
        46.5K
        93.5K
        51.5Kbp
        135.3Mbp
        MEGAHIT-S0154
        0.8Kbp
        0.6Kbp
        48.2K
        94.4K
        35.3Kbp
        123.9Mbp
        MEGAHIT-S0155
        0.9Kbp
        0.6Kbp
        44.8K
        99.4K
        145.6Kbp
        162.6Mbp
        MEGAHIT-S0156
        0.7Kbp
        0.6Kbp
        50.6K
        95.3K
        96.7Kbp
        117.8Mbp
        MEGAHIT-S0157
        1.0Kbp
        0.7Kbp
        43.2K
        100.5K
        134.9Kbp
        178.6Mbp
        MEGAHIT-S0158
        0.9Kbp
        0.6Kbp
        33.7K
        72.7K
        193.2Kbp
        118.5Mbp
        MEGAHIT-S0159
        1.0Kbp
        0.7Kbp
        64.6K
        149.7K
        142.4Kbp
        266.6Mbp
        MEGAHIT-S0160
        1.0Kbp
        0.7Kbp
        108.3K
        246.8K
        302.4Kbp
        432.9Mbp
        MEGAHIT-S0161
        0.9Kbp
        0.6Kbp
        56.3K
        122.4K
        205.7Kbp
        201.5Mbp
        MEGAHIT-S0162
        0.9Kbp
        0.6Kbp
        115.4K
        248.8K
        234.5Kbp
        404.4Mbp
        MEGAHIT-S0163
        1.1Kbp
        0.7Kbp
        52.9K
        136.9K
        206.2Kbp
        283.7Mbp
        MEGAHIT-S0164
        0.9Kbp
        0.6Kbp
        62.9K
        133.0K
        68.0Kbp
        208.3Mbp
        MEGAHIT-S0165
        0.8Kbp
        0.6Kbp
        49.2K
        99.9K
        65.1Kbp
        141.7Mbp
        MEGAHIT-S0166
        0.8Kbp
        0.6Kbp
        54.6K
        109.4K
        35.1Kbp
        156.3Mbp
        MEGAHIT-S0167
        0.8Kbp
        0.6Kbp
        50.0K
        99.3K
        35.6Kbp
        136.9Mbp
        MEGAHIT-S0168
        1.0Kbp
        0.7Kbp
        29.9K
        68.9K
        118.4Kbp
        123.9Mbp
        MEGAHIT-S0169
        0.8Kbp
        0.6Kbp
        34.5K
        68.0K
        29.7Kbp
        93.4Mbp
        MEGAHIT-S0170
        0.9Kbp
        0.6Kbp
        37.3K
        75.3K
        54.3Kbp
        110.2Mbp
        MEGAHIT-S0171
        0.8Kbp
        0.6Kbp
        43.2K
        82.9K
        40.2Kbp
        106.4Mbp
        MEGAHIT-S0172
        0.8Kbp
        0.6Kbp
        71.5K
        141.6K
        95.7Kbp
        196.2Mbp
        MEGAHIT-S0181
        0.8Kbp
        0.6Kbp
        88.4K
        172.3K
        149.7Kbp
        235.2Mbp
        MEGAHIT-S0182
        0.8Kbp
        0.6Kbp
        63.9K
        126.3K
        157.2Kbp
        173.5Mbp
        MEGAHIT-S0183
        0.7Kbp
        0.6Kbp
        51.6K
        96.6K
        28.7Kbp
        116.6Mbp
        MEGAHIT-S0184
        0.8Kbp
        0.6Kbp
        83.9K
        161.5K
        57.6Kbp
        217.5Mbp
        MEGAHIT-S0185
        0.8Kbp
        0.6Kbp
        69.9K
        134.4K
        50.8Kbp
        175.5Mbp
        MEGAHIT-S0186
        0.9Kbp
        0.7Kbp
        70.7K
        154.5K
        111.0Kbp
        258.3Mbp
        MEGAHIT-S0187
        0.8Kbp
        0.6Kbp
        67.0K
        132.2K
        150.8Kbp
        185.2Mbp
        MEGAHIT-S0188
        0.8Kbp
        0.6Kbp
        90.3K
        179.6K
        101.5Kbp
        251.3Mbp
        MEGAHIT-S0189
        0.8Kbp
        0.6Kbp
        75.3K
        150.0K
        67.1Kbp
        210.7Mbp
        MEGAHIT-S0191
        0.8Kbp
        0.6Kbp
        72.0K
        143.7K
        61.1Kbp
        202.7Mbp
        MEGAHIT-S0192
        0.8Kbp
        0.6Kbp
        88.2K
        174.8K
        173.1Kbp
        243.3Mbp
        MEGAHIT-S0193
        0.8Kbp
        0.6Kbp
        51.0K
        105.2K
        109.8Kbp
        152.5Mbp
        MEGAHIT-S0196
        0.8Kbp
        0.6Kbp
        57.1K
        113.2K
        37.4Kbp
        158.8Mbp
        MEGAHIT-S0197
        0.8Kbp
        0.6Kbp
        61.9K
        122.1K
        115.5Kbp
        169.3Mbp
        MEGAHIT-S0198
        0.8Kbp
        0.6Kbp
        38.8K
        77.8K
        122.3Kbp
        106.1Mbp
        MEGAHIT-S0199
        0.8Kbp
        0.6Kbp
        58.2K
        112.8K
        41.5Kbp
        149.8Mbp
        MEGAHIT-S0200
        0.8Kbp
        0.6Kbp
        70.2K
        138.4K
        131.1Kbp
        192.0Mbp
        MEGAHIT-S0201
        0.7Kbp
        0.6Kbp
        20.9K
        37.9K
        8.3Kbp
        43.6Mbp
        MEGAHIT-S0202
        0.8Kbp
        0.6Kbp
        45.6K
        88.8K
        76.5Kbp
        122.0Mbp
        MEGAHIT-S0203
        0.9Kbp
        0.6Kbp
        63.0K
        128.5K
        128.7Kbp
        189.8Mbp
        MEGAHIT-S0204
        0.8Kbp
        0.6Kbp
        86.0K
        169.3K
        32.7Kbp
        239.6Mbp
        MEGAHIT-S0205
        0.8Kbp
        0.6Kbp
        65.4K
        126.2K
        42.6Kbp
        169.1Mbp
        MEGAHIT-S0206
        0.9Kbp
        0.6Kbp
        57.0K
        117.6K
        142.1Kbp
        176.3Mbp
        MEGAHIT-S0207
        0.8Kbp
        0.6Kbp
        47.0K
        87.9K
        15.0Kbp
        111.3Mbp
        MEGAHIT-S0211
        1.0Kbp
        0.7Kbp
        62.7K
        147.0K
        210.4Kbp
        277.6Mbp
        MEGAHIT-S0212
        1.2Kbp
        0.7Kbp
        55.2K
        136.5K
        157.2Kbp
        293.0Mbp
        MEGAHIT-S0213
        1.0Kbp
        0.7Kbp
        53.1K
        120.7K
        115.4Kbp
        221.6Mbp
        MEGAHIT-S0214
        1.0Kbp
        0.7Kbp
        55.2K
        122.7K
        83.9Kbp
        209.4Mbp
        MEGAHIT-S0215
        0.8Kbp
        0.6Kbp
        48.6K
        95.3K
        80.5Kbp
        126.4Mbp
        MEGAHIT-S0216
        0.9Kbp
        0.6Kbp
        62.9K
        127.3K
        53.5Kbp
        186.3Mbp
        MEGAHIT-S0217
        1.0Kbp
        0.7Kbp
        49.8K
        110.2K
        209.9Kbp
        188.4Mbp
        MEGAHIT-S0218
        0.9Kbp
        0.6Kbp
        57.7K
        121.6K
        66.4Kbp
        192.8Mbp
        MEGAHIT-S0219
        0.8Kbp
        0.6Kbp
        49.7K
        101.3K
        73.2Kbp
        148.5Mbp
        MEGAHIT-S0220
        1.0Kbp
        0.7Kbp
        48.4K
        106.9K
        286.0Kbp
        183.3Mbp
        MEGAHIT-S0221
        0.8Kbp
        0.6Kbp
        37.2K
        74.1K
        82.7Kbp
        102.3Mbp
        MEGAHIT-S0222
        0.9Kbp
        0.6Kbp
        45.8K
        92.9K
        52.2Kbp
        137.3Mbp
        MEGAHIT-S0223
        0.8Kbp
        0.6Kbp
        60.7K
        120.4K
        37.8Kbp
        168.6Mbp
        MEGAHIT-S0224
        0.8Kbp
        0.6Kbp
        36.0K
        68.8K
        182.5Kbp
        87.8Mbp
        MEGAHIT-S0225
        0.8Kbp
        0.6Kbp
        38.2K
        73.6K
        50.9Kbp
        96.9Mbp
        MEGAHIT-S0226
        0.9Kbp
        0.6Kbp
        41.0K
        82.7K
        54.8Kbp
        120.4Mbp
        MEGAHIT-S0227
        0.8Kbp
        0.6Kbp
        32.9K
        67.2K
        183.8Kbp
        98.2Mbp
        MEGAHIT-S0228
        0.8Kbp
        0.6Kbp
        54.1K
        107.2K
        92.9Kbp
        145.4Mbp
        MEGAHIT-S0231
        0.8Kbp
        0.6Kbp
        125.2K
        250.7K
        91.7Kbp
        359.9Mbp
        MEGAHIT-S0232
        0.8Kbp
        0.6Kbp
        33.4K
        62.8K
        29.5Kbp
        79.1Mbp
        MEGAHIT-S0233
        0.8Kbp
        0.6Kbp
        46.1K
        94.9K
        95.8Kbp
        140.0Mbp
        MEGAHIT-S0234
        0.8Kbp
        0.6Kbp
        49.7K
        99.4K
        136.6Kbp
        140.2Mbp
        MEGAHIT-S0235
        0.8Kbp
        0.6Kbp
        41.6K
        80.3K
        18.1Kbp
        107.0Mbp
        MEGAHIT-S0236
        0.8Kbp
        0.6Kbp
        67.0K
        130.2K
        26.0Kbp
        179.5Mbp
        MEGAHIT-S0237
        0.8Kbp
        0.6Kbp
        101.6K
        195.1K
        41.7Kbp
        260.0Mbp
        MEGAHIT-S0238
        0.9Kbp
        0.6Kbp
        78.0K
        157.8K
        187.7Kbp
        232.3Mbp
        MEGAHIT-S0239
        0.9Kbp
        0.7Kbp
        78.9K
        175.3K
        174.9Kbp
        296.3Mbp
        MEGAHIT-S0240
        1.1Kbp
        0.7Kbp
        61.3K
        155.9K
        339.8Kbp
        315.6Mbp
        MEGAHIT-S0241
        1.0Kbp
        0.7Kbp
        43.9K
        102.0K
        212.3Kbp
        182.4Mbp
        MEGAHIT-S0242
        1.0Kbp
        0.7Kbp
        40.3K
        96.7K
        321.8Kbp
        176.8Mbp
        MEGAHIT-S0243
        0.9Kbp
        0.6Kbp
        95.2K
        209.5K
        395.7Kbp
        342.7Mbp
        MEGAHIT-S0244
        0.9Kbp
        0.7Kbp
        89.9K
        192.0K
        214.8Kbp
        313.0Mbp
        MEGAHIT-S0245
        0.9Kbp
        0.6Kbp
        58.0K
        117.1K
        280.7Kbp
        173.4Mbp
        MEGAHIT-S0246
        0.8Kbp
        0.6Kbp
        58.3K
        115.4K
        78.5Kbp
        157.7Mbp
        MEGAHIT-S0247
        0.9Kbp
        0.6Kbp
        71.6K
        155.6K
        176.5Kbp
        252.6Mbp
        MEGAHIT-S0248
        0.8Kbp
        0.6Kbp
        59.8K
        122.8K
        62.9Kbp
        179.8Mbp
        MEGAHIT-S0249
        0.8Kbp
        0.6Kbp
        68.8K
        134.8K
        54.8Kbp
        183.0Mbp
        MEGAHIT-S0250
        0.8Kbp
        0.6Kbp
        70.4K
        143.6K
        221.0Kbp
        210.2Mbp
        MEGAHIT-S0273
        0.8Kbp
        0.6Kbp
        56.2K
        107.6K
        60.4Kbp
        141.0Mbp
        MEGAHIT-S0274
        0.8Kbp
        0.6Kbp
        50.8K
        97.4K
        69.6Kbp
        127.9Mbp
        MEGAHIT-S0275
        0.8Kbp
        0.6Kbp
        46.3K
        88.6K
        124.8Kbp
        115.4Mbp
        MEGAHIT-S0276
        0.9Kbp
        0.6Kbp
        26.6K
        55.6K
        48.6Kbp
        84.0Mbp
        MEGAHIT-S0277
        0.8Kbp
        0.6Kbp
        70.9K
        137.9K
        64.6Kbp
        186.3Mbp
        MEGAHIT-S0283
        0.8Kbp
        0.6Kbp
        50.2K
        96.9K
        157.9Kbp
        127.8Mbp
        MEGAHIT-S0284
        0.8Kbp
        0.6Kbp
        48.5K
        93.8K
        67.5Kbp
        123.0Mbp
        MEGAHIT-S0285
        0.8Kbp
        0.6Kbp
        50.7K
        98.3K
        126.4Kbp
        129.7Mbp
        MEGAHIT-S0286
        0.8Kbp
        0.6Kbp
        64.6K
        125.8K
        174.5Kbp
        169.5Mbp
        MEGAHIT-S0287
        0.8Kbp
        0.6Kbp
        65.6K
        132.2K
        52.9Kbp
        191.9Mbp
        MEGAHIT-S0288
        0.8Kbp
        0.6Kbp
        200.4K
        392.3K
        70.2Kbp
        532.0Mbp
        MEGAHIT-S0294
        1.0Kbp
        0.7Kbp
        50.8K
        113.6K
        128.8Kbp
        194.5Mbp
        MEGAHIT-S0295
        0.9Kbp
        0.6Kbp
        65.7K
        139.4K
        101.0Kbp
        222.1Mbp
        MEGAHIT-S0296
        0.9Kbp
        0.6Kbp
        54.5K
        115.3K
        130.4Kbp
        181.3Mbp
        MEGAHIT-S0297
        0.9Kbp
        0.6Kbp
        75.5K
        155.0K
        170.4Kbp
        234.3Mbp
        MEGAHIT-S0298
        0.8Kbp
        0.6Kbp
        43.3K
        90.0K
        360.6Kbp
        131.7Mbp
        MEGAHIT-S0301
        0.8Kbp
        0.6Kbp
        69.5K
        135.4K
        103.1Kbp
        185.6Mbp
        MEGAHIT-S0302
        0.8Kbp
        0.6Kbp
        82.0K
        161.1K
        105.1Kbp
        224.0Mbp
        MEGAHIT-S0303
        0.8Kbp
        0.6Kbp
        79.2K
        155.3K
        74.1Kbp
        214.9Mbp
        MEGAHIT-S0304
        0.8Kbp
        0.6Kbp
        66.4K
        129.7K
        55.4Kbp
        177.5Mbp
        MEGAHIT-S0305
        0.8Kbp
        0.6Kbp
        79.8K
        156.4K
        71.2Kbp
        217.0Mbp
        MEGAHIT-S0355
        0.8Kbp
        0.6Kbp
        105.7K
        202.1K
        120.1Kbp
        265.9Mbp
        MEGAHIT-S0356
        0.8Kbp
        0.6Kbp
        88.8K
        170.1K
        88.5Kbp
        225.2Mbp
        MEGAHIT-S0357
        0.7Kbp
        0.6Kbp
        21.6K
        39.7K
        17.5Kbp
        46.8Mbp
        MEGAHIT-S0358
        0.8Kbp
        0.6Kbp
        50.5K
        96.1K
        35.9Kbp
        123.7Mbp
        MEGAHIT-S0359
        0.8Kbp
        0.6Kbp
        85.3K
        163.2K
        97.2Kbp
        216.2Mbp
        MEGAHIT-S0360
        0.8Kbp
        0.6Kbp
        50.4K
        99.8K
        99.0Kbp
        141.2Mbp
        MEGAHIT-S0420
        0.8Kbp
        0.6Kbp
        67.2K
        129.6K
        159.8Kbp
        171.5Mbp
        MEGAHIT-S0421
        0.8Kbp
        0.6Kbp
        65.9K
        126.9K
        118.1Kbp
        167.7Mbp
        MEGAHIT-S0422
        0.8Kbp
        0.6Kbp
        55.6K
        107.5K
        138.6Kbp
        143.2Mbp
        MEGAHIT-S0423
        0.8Kbp
        0.6Kbp
        47.7K
        91.5K
        124.4Kbp
        119.9Mbp
        MEGAHIT-S0424
        0.8Kbp
        0.6Kbp
        37.3K
        74.1K
        46.3Kbp
        102.9Mbp
        MEGAHIT-S0425
        0.9Kbp
        0.6Kbp
        39.6K
        80.5K
        35.0Kbp
        118.6Mbp
        MEGAHIT-S0447
        1.1Kbp
        0.7Kbp
        57.2K
        133.6K
        343.9Kbp
        255.1Mbp
        MEGAHIT-S0448
        1.1Kbp
        0.7Kbp
        52.0K
        122.8K
        273.7Kbp
        234.7Mbp
        MEGAHIT-S0449
        1.0Kbp
        0.7Kbp
        34.2K
        79.4K
        111.5Kbp
        144.3Mbp
        MEGAHIT-S0450
        0.9Kbp
        0.6Kbp
        23.7K
        53.2K
        84.8Kbp
        90.5Mbp
        MEGAHIT-S0451
        1.0Kbp
        0.7Kbp
        42.9K
        100.1K
        136.7Kbp
        182.0Mbp
        MEGAHIT-S0452
        1.0Kbp
        0.7Kbp
        48.6K
        113.2K
        107.2Kbp
        206.9Mbp
        MEGAHIT-S0459
        1.0Kbp
        0.7Kbp
        71.0K
        153.5K
        185.7Kbp
        256.2Mbp
        MEGAHIT-S0460
        0.9Kbp
        0.6Kbp
        67.2K
        137.0K
        145.1Kbp
        208.9Mbp
        MEGAHIT-S0461
        0.9Kbp
        0.6Kbp
        55.8K
        113.9K
        95.6Kbp
        171.1Mbp
        MEGAHIT-S0462
        0.9Kbp
        0.6Kbp
        53.5K
        109.8K
        104.3Kbp
        166.3Mbp
        MEGAHIT-S0463
        0.7Kbp
        0.6Kbp
        4.9K
        8.9K
        10.6Kbp
        10.1Mbp
        MEGAHIT-S0464
        0.7Kbp
        0.6Kbp
        9.4K
        17.5K
        15.3Kbp
        21.0Mbp
        MEGAHIT-S0465
        0.7Kbp
        0.6Kbp
        6.0K
        11.1K
        9.6Kbp
        13.1Mbp
        MEGAHIT-S0466
        0.7Kbp
        0.6Kbp
        5.8K
        10.7K
        14.0Kbp
        12.6Mbp
        MEGAHIT-S0467
        0.7Kbp
        0.6Kbp
        5.9K
        10.9K
        8.8Kbp
        12.5Mbp
        MEGAHIT-S0468
        0.7Kbp
        0.6Kbp
        2.8K
        4.9K
        4.8Kbp
        5.3Mbp
        MEGAHIT-S0469
        0.7Kbp
        0.6Kbp
        7.9K
        14.8K
        8.9Kbp
        17.9Mbp
        MEGAHIT-S0470
        0.8Kbp
        0.6Kbp
        23.3K
        46.0K
        74.3Kbp
        65.1Mbp
        MEGAHIT-S0471
        0.8Kbp
        0.6Kbp
        8.4K
        16.0K
        16.1Kbp
        20.0Mbp
        MEGAHIT-S0472
        0.9Kbp
        0.6Kbp
        74.3K
        158.5K
        66.6Kbp
        252.0Mbp
        MEGAHIT-S0473
        0.9Kbp
        0.6Kbp
        69.9K
        146.3K
        117.6Kbp
        224.6Mbp
        MEGAHIT-S0474
        0.9Kbp
        0.6Kbp
        257.3K
        549.5K
        188.3Kbp
        858.9Mbp
        MEGAHIT-S0475
        0.9Kbp
        0.6Kbp
        295.2K
        648.0K
        185.2Kbp
        1070.4Mbp
        MEGAHIT-S0476
        0.9Kbp
        0.6Kbp
        136.5K
        298.3K
        184.7Kbp
        494.1Mbp
        MEGAHIT-S0477
        0.9Kbp
        0.6Kbp
        29.1K
        60.8K
        90.4Kbp
        93.1Mbp
        MEGAHIT-S0478
        0.9Kbp
        0.6Kbp
        42.6K
        87.8K
        76.3Kbp
        132.2Mbp
        MEGAHIT-S0479
        0.8Kbp
        0.6Kbp
        33.9K
        68.4K
        50.2Kbp
        99.2Mbp
        MEGAHIT-S0480
        0.9Kbp
        0.6Kbp
        32.4K
        65.3K
        60.1Kbp
        95.4Mbp
        MEGAHIT-S0481
        0.9Kbp
        0.6Kbp
        40.6K
        84.5K
        51.1Kbp
        133.2Mbp
        MEGAHIT-S0482
        0.9Kbp
        0.7Kbp
        60.7K
        130.0K
        77.7Kbp
        214.2Mbp

        Number of Contigs

        This plot shows the number of contigs found for each assembly, broken down by length.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        QUAST: bins

        QUAST: bins Assembly statistics of binned assemblies.DOI: 10.1093/bioinformatics/btt086.

        Assembly Statistics

        Showing 4704 samples.

        loading..

        Number of Contigs

        This plot shows the number of contigs found for each assembly, broken down by length.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Prokka

        Prokka is a software tool for the rapid annotation of prokaryotic genomes.DOI: 10.1093/bioinformatics/btu153.

        This barplot shows the distribution of different types of features found in each contig.

        Prokka can detect different features:

        • CDS
        • rRNA
        • tmRNA
        • tRNA
        • miscRNA
        • signal peptides
        • CRISPR arrays

        This barplot shows you the distribution of these different types of features found in each contig.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        nf-core/mag Methods Description

        Suggested text and references to use when describing pipeline usage within the methods section of a publication.

        Methods

        Data was processed using nf-core/mag v2.5.1 ((doi: 10.1093/nargab/lqac007); Krakau et al., 2022) of the nf-core collection of workflows (Ewels et al., 2020), utilising reproducible software environments from the Bioconda (Grüning et al., 2018) and Biocontainers (da Veiga Leprevost et al., 2017) projects.

        The pipeline was executed with Nextflow v23.10.0 (Di Tommaso et al., 2017) with the following command:

        nextflow run nf-core/mag -profile apptainer --input Samplesheet_input.csv --outdir result_BlendARGs --gtdb_db 'https://data.ace.uq.edu.au/public/gtdb/data/releases/release214/214.1/auxillary_files/gtdbtk_r214_data.tar.gz' --skip_spades --skip_spadeshybrid --skip_maxbin2 --skip_concoct --max_time 30.h --gtdbtk_min_completeness 40.0 --run_virus_identification --busco_db 'https://busco-data.ezlab.org/v5/data/lineages/bacteria_odb10.2024-01-08.tar.gz' --save_busco_db --busco_clean --genomad_db genomad_db -c config_time.nf -resume friendly_mestorf

        References

        • Di Tommaso, P., Chatzou, M., Floden, E. W., Barja, P. P., Palumbo, E., & Notredame, C. (2017). Nextflow enables reproducible computational workflows. Nature Biotechnology, 35(4), 316-319. doi: 10.1038/nbt.3820
        • Ewels, P. A., Peltzer, A., Fillinger, S., Patel, H., Alneberg, J., Wilm, A., Garcia, M. U., Di Tommaso, P., & Nahnsen, S. (2020). The nf-core framework for community-curated bioinformatics pipelines. Nature Biotechnology, 38(3), 276-278. doi: 10.1038/s41587-020-0439-x
        • Krakau, S., Straub, D., Gourlé, H., Gabernet, G., & Nahnsen, S. (2022). nf-core/mag: a best-practice pipeline for metagenome hybrid assembly and binning. NAR Genomics and Bioinformatics, 4(1). https://doi.org/10.1038/s41587-020-0439-x
        • Grüning, B., Dale, R., Sjödin, A., Chapman, B. A., Rowe, J., Tomkins-Tinch, C. H., Valieris, R., Köster, J., & Bioconda Team. (2018). Bioconda: sustainable and comprehensive software distribution for the life sciences. Nature Methods, 15(7), 475–476. doi: 10.1038/s41592-018-0046-7
        • da Veiga Leprevost, F., Grüning, B. A., Alves Aflitos, S., Röst, H. L., Uszkoreit, J., Barsnes, H., Vaudel, M., Moreno, P., Gatto, L., Weber, J., Bai, M., Jimenez, R. C., Sachsenberg, T., Pfeuffer, J., Vera Alvarez, R., Griss, J., Nesvizhskii, A. I., & Perez-Riverol, Y. (2017). BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics (Oxford, England), 33(16), 2580–2582. doi: 10.1093/bioinformatics/btx192
        Notes:
        • The command above does not include parameters contained in any configs or profiles that may have been used. Ensure the config file is also uploaded with your publication!
        • You should also cite all software used within this run. Check the "Software Versions" of this report to get version information.

        nf-core/mag Software Versions

        are collected at run time from the software output.

        Process Name Software Version
        BOWTIE2_ASSEMBLY_ALIGN bowtie2 2.4.2
        pigz 2.3.4
        samtools 1.11
        BOWTIE2_PHIX_REMOVAL_ALIGN bowtie2 2.4.2
        BUSCO R 4.1.3
        busco 5.4.3
        python 3.9.13
        CUSTOM_DUMPSOFTWAREVERSIONS python 3.11.4
        yaml 6.0
        FASTP fastp 0.23.4
        FASTQC_RAW fastqc 0.11.9
        FASTQC_TRIMMED fastqc 0.11.9
        GTDBTK_CLASSIFYWF gtdbtk 2.3.2
        GUNZIP_BINS gunzip 1.10
        MAG_DEPTHS pandas 1.1.5
        python 3.6.7
        MAG_DEPTHS_PLOT pandas 1.3.0
        python 3.9.6
        seaborn 0.11.0
        MAG_DEPTHS_SUMMARY pandas 1.4.3
        python 3.10.6
        MEGAHIT megahit 1.2.9
        METABAT2_JGISUMMARIZEBAMCONTIGDEPTHS metabat2 2.15
        METABAT2_METABAT2 metabat2 2.15
        PRODIGAL pigz 2.6
        prodigal 2.6.3
        PROKKA prokka 1.14.6
        QUAST metaquast 5.0.2
        python 3.7.6
        QUAST_BINS metaquast 5.0.2
        python 3.7.6
        SPLIT_FASTA biopython 1.7.4
        pandas 1.1.5
        python 3.6.7
        Workflow Nextflow 23.10.0
        nf-core/mag 2.5.1

        nf-core/mag Workflow Summary

        - this information is collected when the pipeline is started.

        Core Nextflow options

        revision
        master
        runName
        curious_borg
        containerEngine
        apptainer
        launchDir
        /cephyr/NOBACKUP/groups/jbp/matev/OceanARM/BlendARGs
        workDir
        /cephyr/NOBACKUP/groups/jbp/matev/OceanARM/BlendARGs/work
        projectDir
        /cephyr/users/matev/Vera/.nextflow/assets/nf-core/mag
        userName
        matev
        profile
        apptainer
        configFiles
        N/A

        Input/output options

        input
        Samplesheet_input.csv
        outdir
        result_BlendARGs

        Max job request options

        max_time
        30.h

        Quality control for short reads options

        phix_reference
        /cephyr/users/matev/Vera/.nextflow/assets/nf-core/mag/assets/data/GCA_002596845.1_ASM259684v1_genomic.fna.gz

        Quality control for long reads options

        lambda_reference
        /cephyr/users/matev/Vera/.nextflow/assets/nf-core/mag/assets/data/GCA_000840245.1_ViralProj14204_genomic.fna.gz

        Taxonomic profiling options

        gtdbtk_min_completeness
        40.0
        gtdbtk_min_perc_aa
        10
        gtdbtk_pplacer_cpus
        1
        genomad_db
        genomad_db

        Assembly options

        skip_spades
        true
        skip_spadeshybrid
        true

        Virus identification options

        run_virus_identification
        true

        Binning options

        skip_maxbin2
        true
        skip_concoct
        true

        Bin quality check options

        busco_db
        https://busco-data.ezlab.org/v5/data/lineages/bacteria_odb10.2024-01-08.tar.gz
        save_busco_db
        true
        busco_clean
        true